Feature extraction generally falls into two main categories: 2D or 3D Convolutional Neural Network (CNN) based methods. 2D CNN methods utilise a 2D CNN [1]}, [2]}, [3]} backbone to extract frame level predictions with a temporal aggregation step, such as a simple averaging [4]} or a more complex operation such as an LSTM block [5]} to produce features for a set of input frames. In contrast, 3D CNN based methods ([6]}, [7]}, [8]}, [9]}, [10]}) expand the 2D convolutional operation into the temporal dimension, performing 3D convolutions across space and time, extracting joint spatial and temporal semantics from voxels of raw video data. Other works utilise the inherent computational efficiency of 2D CNNs via the incorporation of temporally aware modules inserted within 2D CNN architectures ([11]}, [12]}) facilitating the mixing of features across frames, allowing for a computationally efficient and parameter light methods of facilitating temporal learning.
